Abstract
The efficiency of A searching depends on the quality of the lower bound estimates of the solution cost. Pattern databases enumerate all possible subgoals required by any solution, subject to constraints on the subgoal size. Each subgoal in the database provides a tight lower bound on the cost of achieving it. For a given state in the search space, all possible subgoals are looked up, with the maximum cost over all lookups being the lower bound. For sliding tile puzzles, the database enumerates all possible patterns containing N tiles and, for each one, contains a lower bound on the distance to correctly move all N tiles into their correct final location. For the 15-Puzzle, iterative-deepening A with pattern databases (N=8) reduces the total number of nodes searched on a standard problem set of 100 positions by over 1000-fold.
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© 1996 Springer-Verlag Berlin Heidelberg
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Culberson, J.C., Schaeffer, J. (1996). Searching with pattern databases. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_68
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DOI: https://doi.org/10.1007/3-540-61291-2_68
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